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However, converting their inherent textual uncertainty into reliable information for decision support remains a significant challenge. This paper proposes a rough\u2010cloud\u2010integrated multiattribute three\u2010way decision (MATWD) framework that captures the uncertainty of online reviews and supports product classification and ranking. First, a rough\u2010cloud model is developed to transform sentiment orientations from online reviews into normal cloud representations, capturing both intrapersonal uncertainty and interpersonal uncertainty inherent in user\u2010generated content. Second, we introduce symmetric Kullback\u2013Leibler divergence and probability of superiority as two novel measurements for cloud models, which enable the derivation of relative profit functions, risk avoidance coefficients, and conditional probabilities within a cloud\u2010based three\u2010way decision framework. Third, a cloud\u2010based indifference threshold\u2010based attribute ratio analysis (ITARA) method is developed to determine objective attribute weights, which are then integrated into the MATWD framework to obtain three\u2010way classifications and, subsequently, a comprehensive ranking of alternatives via the Borda count method. Finally, the proposed methodology is validated through a case study on new energy vehicle selection, demonstrating its practical applicability and effectiveness.<\/jats:p>","DOI":"10.1155\/int\/4162038","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:39:13Z","timestamp":1777451953000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Online Review\u2010Driven Multiattribute Decision\u2010Making Model Based on Rough\u2010Cloud\u2010Integrated Three\u2010Way Decisions"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-1646","authenticated-orcid":false,"given":"Fan","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5269-4927","authenticated-orcid":false,"given":"Xuhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2626-1837","authenticated-orcid":false,"given":"Mingrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"Big Data: A Revolution That Will Transform How We Live, Work, and Think","author":"Mayer-Sch\u00f6nberger V.","year":"2013"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.02.007"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2020.3604"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2022.102784"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2024.103197"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11518-018-5388-2"},{"key":"e_1_2_9_7_2","article-title":"An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features","author":"Amirifar T.","year":"2023","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"PangB. 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